



'''simple docstring'''






import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         MODEL_MAPPING,
         SegformerForImageClassification,
         SegformerForSemanticSegmentation,
         SegformerModel,
     )
     from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import SegformerImageProcessor

class 				a__(   lowerCamelCase__      ):







    def      lowercase_			( self					:    Union[str, Any]      ):
      a		:   Union[str, Any]	  =	self.config_class(**self.inputs_dict      )
      self.parent.assertTrue(hasattr(__snake_case		,     'hidden_sizes'      )      )
      self.parent.assertTrue(hasattr(__snake_case		,     'num_attention_heads'      )      )
      self.parent.assertTrue(hasattr(__snake_case		,     'num_encoder_blocks'      )      )

class 				a__:







    def __init__( self					:    str		,     __snake_case					:    List[str]		,     __snake_case					:    Union[str, Any]=13		,     __snake_case					:    Optional[Any]=64		,     __snake_case					:    int=3		,     __snake_case					:    str=4		,     __snake_case					:    Optional[int]=[2, 2, 2, 2]		,     __snake_case					:    Optional[Any]=[8, 4, 2, 1]		,     __snake_case					:    int=[16, 32, 64, 1_28]		,     __snake_case					:    Tuple=[1, 4, 8, 16]		,     __snake_case					:    Union[str, Any]=[1, 2, 4, 8]		,     __snake_case					:    Tuple=True		,     __snake_case					:    Tuple=True		,     __snake_case					:    List[str]="gelu"		,     __snake_case					:    Union[str, Any]=0.1		,     __snake_case					:    List[str]=0.1		,     __snake_case					:    int=0.02		,     __snake_case					:    List[str]=3		,     __snake_case					:    Tuple=None		,     ):
      a		:   Optional[Any]	  =	parent
      a		:   List[str]	  =	batch_size
      a		:   Dict	  =	image_size
      a		:   Optional[int]	  =	num_channels
      a		:   Optional[Any]	  =	num_encoder_blocks
      a		:   Dict	  =	sr_ratios
      a		:   Tuple	  =	depths
      a		:   Optional[Any]	  =	hidden_sizes
      a		:   str	  =	downsampling_rates
      a		:   List[Any]	  =	num_attention_heads
      a		:   List[Any]	  =	is_training
      a		:   Tuple	  =	use_labels
      a		:   Tuple	  =	hidden_act
      a		:   int	  =	hidden_dropout_prob
      a		:   Dict	  =	attention_probs_dropout_prob
      a		:   Optional[Any]	  =	initializer_range
      a		:   List[str]	  =	num_labels
      a		:   Union[str, Any]	  =	scope







    def      lowercase_			( self					:    Optional[int]      ):
      a		:   Optional[int]	  =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

      a		:   List[Any]	  =	None
      if self.use_labels:
        a		:   List[str]	  =	ids_tensor([self.batch_size, self.image_size, self.image_size]		,     self.num_labels      )

      a		:   List[Any]	  =	self.get_config()
      return config, pixel_values, labels







    def      lowercase_			( self					:    Optional[int]      ):
      return SegformerConfig(
          image_size=self.image_size		,     num_channels=self.num_channels		,     num_encoder_blocks=self.num_encoder_blocks		,     depths=self.depths		,     hidden_sizes=self.hidden_sizes		,     num_attention_heads=self.num_attention_heads		,     hidden_act=self.hidden_act		,     hidden_dropout_prob=self.hidden_dropout_prob		,     attention_probs_dropout_prob=self.attention_probs_dropout_prob		,     initializer_range=self.initializer_range		,     )







    def      lowercase_			( self					:    int		,     __snake_case					:    Optional[Any]		,     __snake_case					:    Any		,     __snake_case					:    List[Any]      ):
      a		:   Union[str, Any]	  =	SegformerModel(config=__snake_case      )
      model.to(__snake_case      )
      model.eval()
      a		:   Optional[int]	  =	model(__snake_case      )
      a		:   Optional[Any]	  =	self.image_size // (self.downsampling_rates[-1] * 2)
      self.parent.assertEqual(
          result.last_hidden_state.shape		,     (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)      )







    def      lowercase_			( self					:    Optional[int]		,     __snake_case					:    List[Any]		,     __snake_case					:    Any		,     __snake_case					:    List[Any]      ):
      a		:   Optional[Any]	  =	self.num_labels
      a		:   str	  =	SegformerForSemanticSegmentation(__snake_case      )
      model.to(__snake_case      )
      model.eval()
      a		:   int	  =	model(__snake_case      )
      self.parent.assertEqual(
          result.logits.shape		,     (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)      )
      a		:   List[str]	  =	model(__snake_case		,     labels=__snake_case      )
      self.parent.assertEqual(
          result.logits.shape		,     (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)      )
      self.parent.assertGreater(result.loss		,     0.0      )







    def      lowercase_			( self					:    Any		,     __snake_case					:    Union[str, Any]		,     __snake_case					:    int		,     __snake_case					:    Any      ):
      a		:   Dict	  =	1
      a		:   Dict	  =	SegformerForSemanticSegmentation(config=__snake_case      )
      model.to(__snake_case      )
      model.eval()
      a		:   List[str]	  =	torch.randint(0		,     1		,     (self.batch_size, self.image_size, self.image_size)      ).to(__snake_case      )
      a		:   List[Any]	  =	model(__snake_case		,     labels=__snake_case      )
      self.parent.assertGreater(result.loss		,     0.0      )







    def      lowercase_			( self					:    List[str]      ):
      a		:   List[str]	  =	self.prepare_config_and_inputs()
      a  ,      a  ,      a		:   Tuple	  =	config_and_inputs
      a		:   Dict	  =	{'pixel_values': pixel_values}
      return config, inputs_dict

@require_torch
class 				a__(   lowerCamelCase__						,					lowerCamelCase__						,					unittest.TestCase      ):
    lowercase__					=       (
        (
            SegformerModel,
            SegformerForSemanticSegmentation,
            SegformerForImageClassification,
        )
        if is_torch_available()
        else ()
    )
    lowercase__					=       (
        {
            """feature-extraction""": SegformerModel,
            """image-classification""": SegformerForImageClassification,
            """image-segmentation""": SegformerForSemanticSegmentation,
        }
        if is_torch_available()
        else {}
    )

    lowercase__					=       True
    lowercase__					=       False
    lowercase__					=       False
    lowercase__					=       False







    def      lowercase_			( self					:    Optional[int]      ):
      a		:   Optional[Any]	  =	SegformerModelTester(self      )
      a		:   List[Any]	  =	SegformerConfigTester(self		,     config_class=__snake_case      )







    def      lowercase_			( self					:    Dict      ):
      self.config_tester.run_common_tests()







    def      lowercase_			( self					:    str      ):
      a		:   Dict	  =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__snake_case      )







    def      lowercase_			( self					:    int      ):
      a		:   Any	  =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_binary_image_segmentation(*__snake_case      )







    def      lowercase_			( self					:    int      ):
      a		:   Dict	  =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_segmentation(*__snake_case      )







    @unittest.skip('SegFormer does not use inputs_embeds'      )
    def      lowercase_			( self					:    List[str]      ):
      pass







    @unittest.skip('SegFormer does not have get_input_embeddings method and get_output_embeddings methods'      )
    def      lowercase_			( self					:    str      ):
      pass







    def      lowercase_			( self					:    Optional[int]      ):
      a  ,      a		:   str	  =	self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        a		:   Union[str, Any]	  =	model_class(__snake_case      )
        a		:   Optional[Any]	  =	inspect.signature(model.forward      )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        a		:   Any	  =	[*signature.parameters.keys()]

        a		:   Optional[int]	  =	['pixel_values']
        self.assertListEqual(arg_names[:1]		,     __snake_case      )







    def      lowercase_			( self					:    str      ):
      a  ,      a		:   List[Any]	  =	self.model_tester.prepare_config_and_inputs_for_common()
      a		:   Any	  =	True

      for model_class in self.all_model_classes:
        a		:   Tuple	  =	True
        a		:   Tuple	  =	False
        a		:   str	  =	True
        a		:   Optional[int]	  =	model_class(__snake_case      )
        model.to(__snake_case      )
        model.eval()
        with torch.no_grad():
          a		:   Dict	  =	model(**self._prepare_for_class(__snake_case		,     __snake_case      )      )
        a		:   Dict	  =	outputs.attentions

        a		:   Union[str, Any]	  =	sum(self.model_tester.depths      )
        self.assertEqual(len(__snake_case      )		,     __snake_case      )

        # check that output_attentions also work using config
        del inputs_dict["output_attentions"]
        a		:   List[Any]	  =	True
        a		:   str	  =	model_class(__snake_case      )
        model.to(__snake_case      )
        model.eval()
        with torch.no_grad():
          a		:   Optional[int]	  =	model(**self._prepare_for_class(__snake_case		,     __snake_case      )      )
        a		:   Union[str, Any]	  =	outputs.attentions

        self.assertEqual(len(__snake_case      )		,     __snake_case      )

        # verify the first attentions (first block, first layer)
        a		:   str	  =	(self.model_tester.image_size // 4) ** 2
        a		:   Union[str, Any]	  =	(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
        self.assertListEqual(
            list(attentions[0].shape[-3:]      )		,     [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]		,     )

        # verify the last attentions (last block, last layer)
        a		:   List[Any]	  =	(self.model_tester.image_size // 32) ** 2
        a		:   List[str]	  =	(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
        self.assertListEqual(
            list(attentions[-1].shape[-3:]      )		,     [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]		,     )
        a		:   List[Any]	  =	len(__snake_case      )

        # Check attention is always last and order is fine
        a		:   Optional[int]	  =	True
        a		:   str	  =	True
        a		:   Optional[int]	  =	model_class(__snake_case      )
        model.to(__snake_case      )
        model.eval()
        with torch.no_grad():
          a		:   Dict	  =	model(**self._prepare_for_class(__snake_case		,     __snake_case      )      )

        self.assertEqual(out_len + 1		,     len(__snake_case      )      )

        a		:   Dict	  =	outputs.attentions

        self.assertEqual(len(__snake_case      )		,     __snake_case      )
        # verify the first attentions (first block, first layer)
        a		:   Tuple	  =	(self.model_tester.image_size // 4) ** 2
        a		:   Optional[Any]	  =	(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
        self.assertListEqual(
            list(self_attentions[0].shape[-3:]      )		,     [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]		,     )







    def      lowercase_			( self					:    List[str]      ):
      def check_hidden_states_output(__snake_case					:    Optional[Any]		,     __snake_case					:    str		,     __snake_case					:    Dict      ):
        a		:   List[str]	  =	model_class(__snake_case      )
        model.to(__snake_case      )
        model.eval()

        with torch.no_grad():
          a		:   Optional[int]	  =	model(**self._prepare_for_class(__snake_case		,     __snake_case      )      )

        a		:   List[str]	  =	outputs.hidden_states

        a		:   List[str]	  =	self.model_tester.num_encoder_blocks
        self.assertEqual(len(__snake_case      )		,     __snake_case      )

        # verify the first hidden states (first block)
        self.assertListEqual(
            list(hidden_states[0].shape[-3:]      )		,     [
                self.model_tester.hidden_sizes[0],
                self.model_tester.image_size // 4,
                self.model_tester.image_size // 4,
            ]		,     )

      a  ,      a		:   Dict	  =	self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        a		:   Any	  =	True
        check_hidden_states_output(__snake_case		,     __snake_case		,     __snake_case      )

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        a		:   Dict	  =	True

        check_hidden_states_output(__snake_case		,     __snake_case		,     __snake_case      )







    def      lowercase_			( self					:    Dict      ):
      if not self.model_tester.is_training:
        return

      a  ,      a		:   List[str]	  =	self.model_tester.prepare_config_and_inputs_for_common()
      a		:   List[Any]	  =	True

      for model_class in self.all_model_classes:
        if model_class in get_values(__snake_case      ):
          continue

        a		:   Any	  =	model_class(__snake_case      )
        model.to(__snake_case      )
        model.train()
        a		:   Optional[Any]	  =	self._prepare_for_class(__snake_case		,     __snake_case		,     return_labels=__snake_case      )
        a		:   str	  =	model(**__snake_case      ).loss
        loss.backward()







    @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'      )
    def      lowercase_			( self					:    Dict      ):
      pass







    @slow
    def      lowercase_			( self					:    Tuple      ):
      for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        a		:   Union[str, Any]	  =	SegformerModel.from_pretrained(__snake_case      )
        self.assertIsNotNone(__snake_case      )
def  lowerCamelCase__			(		):
  a		:   Optional[Any]	  =	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
  return image

@require_torch
class 				a__(   unittest.TestCase      ):







    @slow
    def      lowercase_			( self					:    Optional[int]      ):
      # only resize + normalize
      a		:   List[Any]	  =	SegformerImageProcessor(
          image_scale=(5_12, 5_12)		,     keep_ratio=__snake_case		,     align=__snake_case		,     do_random_crop=__snake_case      )
      a		:   Optional[Any]	  =	SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512'      ).to(
          __snake_case      )

      a		:   Tuple	  =	prepare_img()
      a		:   Tuple	  =	image_processor(images=__snake_case		,     return_tensors='pt'      )
      a		:   int	  =	encoded_inputs.pixel_values.to(__snake_case      )

      with torch.no_grad():
        a		:   Union[str, Any]	  =	model(__snake_case      )

      a		:   List[str]	  =	torch.Size((1, model.config.num_labels, 1_28, 1_28)      )
      self.assertEqual(outputs.logits.shape		,     __snake_case      )

      a		:   str	  =	torch.tensor(
          [
              [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
              [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
              [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
          ]      ).to(__snake_case      )
      self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]		,     __snake_case		,     atol=1e-4      )      )







    @slow
    def      lowercase_			( self					:    str      ):
      # only resize + normalize
      a		:   List[str]	  =	SegformerImageProcessor(
          image_scale=(5_12, 5_12)		,     keep_ratio=__snake_case		,     align=__snake_case		,     do_random_crop=__snake_case      )
      a		:   Any	  =	SegformerForSemanticSegmentation.from_pretrained(
          'nvidia/segformer-b1-finetuned-cityscapes-1024-1024'      ).to(__snake_case      )

      a		:   str	  =	prepare_img()
      a		:   Tuple	  =	image_processor(images=__snake_case		,     return_tensors='pt'      )
      a		:   Optional[int]	  =	encoded_inputs.pixel_values.to(__snake_case      )

      with torch.no_grad():
        a		:   List[str]	  =	model(__snake_case      )

      a		:   str	  =	torch.Size((1, model.config.num_labels, 1_28, 1_28)      )
      self.assertEqual(outputs.logits.shape		,     __snake_case      )

      a		:   List[str]	  =	torch.tensor(
          [
              [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
              [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
              [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
          ]      ).to(__snake_case      )
      self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]		,     __snake_case		,     atol=1e-1      )      )







    @slow
    def      lowercase_			( self					:    Optional[Any]      ):
      # only resize + normalize
      a		:   int	  =	SegformerImageProcessor(
          image_scale=(5_12, 5_12)		,     keep_ratio=__snake_case		,     align=__snake_case		,     do_random_crop=__snake_case      )
      a		:   Any	  =	SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512'      ).to(
          __snake_case      )

      a		:   Dict	  =	prepare_img()
      a		:   Tuple	  =	image_processor(images=__snake_case		,     return_tensors='pt'      )
      a		:   Any	  =	encoded_inputs.pixel_values.to(__snake_case      )

      with torch.no_grad():
        a		:   Optional[int]	  =	model(__snake_case      )

      a		:   Optional[int]	  =	outputs.logits.detach().cpu()

      a		:   Optional[Any]	  =	image_processor.post_process_semantic_segmentation(outputs=__snake_case		,     target_sizes=[(5_00, 3_00)]      )
      a		:   str	  =	torch.Size((5_00, 3_00)      )
      self.assertEqual(segmentation[0].shape		,     __snake_case      )

      a		:   List[Any]	  =	image_processor.post_process_semantic_segmentation(outputs=__snake_case      )
      a		:   int	  =	torch.Size((1_28, 1_28)      )
      self.assertEqual(segmentation[0].shape		,     __snake_case      )




'''simple docstring'''






from __future__ import annotations
def  lowerCamelCase__			(		_A					,     _A					,     _A							):
  a		:   List[str]	  =	list(range(len(_A							)							)							)
  a		:   Union[str, Any]	  =	[v / w for v, w in zip(_A					,     _A							)]
  index.sort(key=lambda _A							: ratio[i]					,     reverse=_A							)

  a		:   float	  =	0
  a		:   list[float]	  =	[0] * len(_A							)
  for i in index:
    if weight[i] <= capacity:
      a		:   int	  =	1
      max_value += value[i]
      capacity -= weight[i]
    else:
      a		:   Optional[int]	  =	capacity / weight[i]
      max_value += value[i] * capacity / weight[i]
      break

  return max_value, fractions


if __name__ == "__main__":
     import doctest

     doctest.testmod()